day_1_basics_of_R_code.qmd

---
title: "Basics of R code"
author: "Steen Flammild Harsted"
date: '`r Sys.Date()`'
output:
  distill::distill_article:
    self_contained: false
creative_commons: CC BY
format:
  html: 
    code-fold: true
    code-summary: "Show me the code"
    toc: true
    toc-depth: 2
    number-sections: true
    number-depth: 2
editor: 
  mode: source
execute: 
  output: false
---

```{r setup}
#| include: false
library(tidyverse)
```


\ \ \ 

# Functions 

## `<-`

* Do you have a short cut for the assignment operator? (Tools -> Show Command Palette -> and type “assignment”)
* What does the assign operator `<-` do?

<br>

### Assign the inbuilt datset `iris` to an object called `my_data`
`iris` is available to you when you load R. It´s an inbuilt dataset. So it is available to you even though you cant find it in the Environment pane. Just type `iris`.
```{r}
my_data <- iris
```

<br>

### print `my_data`
```{r}
#| results: false
my_data
```

\ \

## `c()`
The c is short for [concatenate](https://www.merriam-webster.com/dictionary/concatenate), and means to link together.  
This function combines values into a vector or list. For now you can think of a vector as a sequence of values.
The values are seperated by a `,`

<br>  
  
### Use `c()` to create a sequence of numbers from 0 to 4
```{r}
c(0, 1, 2, 3, 4)
```
  
### Use `c()` to create a sequence of numbers from 0 to 4 in steps of 2
```{r}
c(0, 2, 4)
```
  

\ \

## `seq()` 
Of course, such predictable operations can be done with ease in a more reliable way. 
We will use the function `seq()` as an example. The output of this function is a sequence of numbers. The sequence of numbers is dictated by the arguments that you provide.

Read the arguments section on help page for `seq()`  
Type `?seq()` in the console or type `seq()`and pres F1 while the cursor stands on the on the letters.

<br>  
  
### Make a sequence of numbers that goes from 0 to 100

```{r}
#| results: false
seq(from = 0,
    to = 100)

# This simple operation can also be done by just writing 0:100
```

<br>  
  
### Make a sequence of numbers that goes from 0 to 100 in steps of 2
```{r}
#| results: false
seq(from = 0,
    to = 100,
    by = 2)
```
  
\ \

## `sample()`

### Using `sample()`, take a sample of 5 random numbers between 1 and 100
```{r}
sample(1:100, 5)
```

### What does the argument `replace` do? What is the default value?

\ \ 


## `mean()`
Another function we can use is `mean()`. This function gives you the mean value of a sequence of numbers.
Read the arguments section of the help page for `mean()`

<br>

### What type of input does the first arguments of the `mean()` function require?
```{r}
# It requires a vector. 
# You can still think of this as a sequence of numbers
# c()
```

<br>

### Using `c()`, take the mean of the numbers 5, 3, 1, and 10
```{r}
mean(c(5, 3, 1, 10))
```

<br>  

### Take the mean of this vector `c(2, 4, 6, NA)`


```{r}
mean(c(2, 4, 6, NA))
```

<br>

### Take the mean of this vector `c(2, 4, 6, NA)`, disregarding `NA` values

::: {.callout-tip collapse=true}
#### Hint
Read about the `na.rm` argument in the `mean()` function. 
What is the default value?
:::

```{r}
mean(c(2, 4, 6, NA),
     na.rm = TRUE)
```

<br>

### You will find the `na.rm` argument in many functions. It always defaults to FALSE. Discuss if this is a good idea? Is it different from other programs?


\ \

## `%>%`

* What is your keyboard shortcut to write the pipe `%>%` ?
(Pres CTRL+SHIFT+P and type **pipe** in the search field).  

### Load the `tidyverse`
Why? The `%<%` is a part of the tidyverse, and is not included in Base R
```{r}
#| code-fold: false
library(tidyverse)
```

<br>

### **Using the pipe,** create a sequence of numbers going from 0 til 100 and then take the mean of the sequence.
```{r}
seq(from = 0,
    to = 100) %>%
  mean()
```

<br>

### **Using the pipe,** create a sequence of numbers going from 1 to 20 in 39 steps, and then sample 10 random numbers from the sequence
```{r}
seq(from = 1,
    to = 20,
    length.out = 39) %>% 
  sample(10)
```

<br>

### Rewrite this code to use the pipe instead
```{r}
#| code-fold: false
sort(
  sample(seq(1:100),
         10,
         replace = TRUE)
  )
```

```{r}
seq(1:100) %>% 
  sample(10,
         replace = TRUE) %>% 
  sort()
```


\ \ 

## Explore
Investigate the following functions that we may need later on in this course.  

* `quantile()`
* `rnorm()`
* `median()`  
* `cumsum()`
* `min()`  
* `max()`  
* `n()`
* `set.seed()`



\ \ \

  
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